Data utilization continues to increase exponentially with the proliferation of mobile devices including, for example, mobile phones, tablet devices, laptop computers and wearable technology. Faster and wider reaching networks allow for increasing use of bandwidth intensive applications. In addition to the increased use of mobile devices, other devices and systems are increasing data usage. For example, smart cars, smart homes and smart appliances consume data with machine to machine (M2M) communication systems as the breadth and scope of available applications increases.
But the proliferation of smart devices and systems is not the only reason that data usage has dramatically increased. The manner and the frequency which smart devices are utilized by individuals have also dramatically increased. For example, the line between personal and corporate data use quite often overlaps as individuals demand to enjoy the benefits of an always connected (anytime, anywhere, and on any device) world. For many, the use of data and connectivity is considered an essential part of their lives. It is not uncommon for someone to notice their mobile phone missing before they would notice a missing wallet or set of keys as individuals are quite often checking their mobile device every few minutes. In fact, smart phones and devices are constantly providing new emails, texts, tweets, posts and notifications on an ongoing basis in addition to providing the traditional function of routing phone calls. The key factor is that data usage is increasing and the cost involved with data usage can be significant. Further, it is increasingly difficult to determine what the data is being used for. Historically, with the first devices came out, email usage was all that determined data use. Then web browsers allowed users to consume data via web browsing. Today, there are a variety of available applications which makes for a wide range of data usage, and it is useful to know how this data is accessed and to categorize the usage by the types of transactions.
For some corporate activities, the costs associated with particular activities are tracked so that the cost can be allocated appropriately. Consider how the tracking and management of voice calls has evolved to include a level of granularity that supports charge-backs, allocation and personal or business cost tracking. Today's voice call tracking systems employ CDR (Call detail records) to keep track of individual voice calls. Using today's available methods from a PBX (Private Branch Exchange), call managers, and call accounting systems; each phone call is captured along with information pertaining to the call. For example, the caller (perhaps a physical desk for a land line call, or perhaps a code which is entered before making calls), the destination number, the length of the call, and the tariffs associated with the call. This record provides information for cost allocation based on tariff tables available from the carrier or service provider, or other pricing tables.
The cost for each call can then be allocated to a specific person or business entity. The call initiator and his/her department may assume the cost, or perhaps the cost is allocated to a customer or project based on the destination of the call. By knowing the time or length of the call and the destination phone number, it may be possible to track and determine if calls are personal or business related. Further, the destination numbers of customer contacts or customers corporate offices can help determine to which customers the cost should be allocated and reclaimed from.
Calling and usage trends and patterns can also be developed from this information to determine load or capacity across any date/time periods. Additionally, this voice call information can be combined with other business performance statistics to assist in determining positive and negative performance, causal traits and best practices that can then be applied to change the behaviour of individuals and/or business entities across the enterprise. If only a summary bill was provided at the end of the month, it would be impossible or at least very difficult and imprecise to attempt to allocate costs on a per voice call basis without the transaction level reporting.
In many office environments, even the process of tracking copier costs has evolved to include counters for departmental chargeback and cost allocation. Consider a mail room in a company where shipments are tracked; costs are allocated to the departments initiating the requests and perhaps re-claimed from the recipients of the letters and packages. Each package could be considered a transaction with a size (perhaps weight, and physical size) which determines the cost associated with the package and the postage necessary to send. The costs for all of these transactions (sending the packages) can be allocated appropriately.
Now imagine if that mail room could only tell when it was out of stamps, or worse yet, could only tell that there was a larger than expected bill that came in for last month from the post office. Perhaps the mail room clerk was mailing all of his personal Christmas cards and those of his friends using the company's postage meter. This is the current state that many corporations find themselves in with regard to their data usage, for example mobile communications data usage and associated costs. There is little or no visibility for what kinds of usage transactions are being made.
Just like the packages and letters in the above-described scenario, individual emails, posts, and other events that use data can be construed as individual transactions with an associated cost when looking at data usage. Some of these transactions are personal, other transactions are business related, and perhaps even in each category it is interesting to allocate some cost to clients, departments, or even campaigns/projects. However, to achieve a similar level of allocation as discussed in the mail room example, data usage information must be obtained on a transactional level.
Surprisingly when we consider overall costs, it's likely that the data usage costs for a company are significantly more expensive than copier and mail room costs in many office environments. For example, data usage may cost as much as ten to twenty times the cost of copiers and mail rooms. But, this data usage cannot be tracked with sufficient detail and reporting. More controls and the ability to manage, track and/or allocate the costs for data transactions are very much needed.
Further, when overages occur in the mail room or the copier or with CDRs, audits can be performed and the root cause of the overage can be traced to a clear set of transactions. Where appropriate, bills can be contested or action can be taken to avoid overage charges in the future. Unfortunately in most of today's overages related to data usage, there is a lack of tools and audit capabilities. Therefore, the enterprise has little or no recourse in challenging or even adjusting practices or usage in order to avoid data overages, because specific instances of data usage cannot be analyzed on a granular level. Accordingly, it is difficult if not impossible to determine the root cause of overages on a transactional level.
Data use and cost models can be analyzed and developed just like the example discussed above. An office environment can determine the overall cost of the infrastructure and bandwidth provided. In some cases data use may be limited to a total usage cost (e.g., 10 GB/month costs ‘X’ amount with overage charges of ‘Y’ amount). In other cases it may be cost per use, where the total usage is itemized at the end of the month and cost derived from a formula based on usage. In other cases it may simply be a fixed cost. Other cost models exist and these are provided to just illustrate how some Internet Service Providers (ISP) formulate their contracts.
The problem that most companies face is that, unlike the mail room example where each transaction can be monitored and cost can be allocated accordingly, data transactions cannot currently be effectively monitored. Most of the existing reporting systems for data use from service providers today offer a means of obtaining statistics or metrics to capture aggregated usage and overall cost. These metrics are typically limited to simple aggregate amounts of data used over time, employing units of measure associated with the user's bandwidth allocation and plan costs. The concept of data usage totals or costs per Megabyte are not particularly helpful as this does not provide sufficient detail about the data usage. As such, there is no effective means of gathering detailed usage events with data transactions for the allocation of costs or for the running of reports.
Additionally, with the growing trend of Over The Top (OTT) type applications that offer alternatives to a carrier's traditional SMS and Voice services, the growing number of OTT transactions also removes the ability for the carrier's systems to track and report on activity with traditional Call Detail Records (CDR), or SMS usage reports. Some examples of OTT type applications may include Facebook, Skype and other messaging or communication applications. While OTT may benefit end users by avoiding the costs of using services such as SMS or traditional Voice services and replacing them with “data based” alternatives, it also reduces visibility to the user's activities.
What is desired then is a system and method for monitoring, capturing, and identifying detailed data usage events (or data transactions).
What is also desired is a system and method for classifying data usage after the detailed usage events have been identified.
What is further desired is a system and method for allocating costs for data usage based on an identification of the detailed usage events.
What is further desired is a system and method for reporting and trending on data usage based on an identification of the detailed usage events. What is further desired is a system and method for determining and optimizing the effectiveness and efficiency of detailed usage events.
What is further desired is a system and method for determining, tracking, and optimizing the effectiveness and efficiency of the behaviour of the originating entity or device generating the usage transaction(s).
What is further desired is a system and method for controlling data usage by allowing usage when the cost can be allocated and thus charged to a particular individual, enterprise or service provider.